Weed dynamics under diverse nutrient management and crop rotation practices in the dry zone of Sri Lanka
Bibliographic record
Abstract
Integrated weed control strategies are essential for organic and integrated nutrient management, where both systems are progressing with a fundamental of zero or minimum synthetic chemical cultivations. For optimizing the outcome of weed management, a better understanding of the weed dynamic is needed. Especially, with the absence of herbicides, weeds are expected to be controlled by the system itself, during the transition period under rice-based crop rotation systems. This study was conducted to estimate the weed abundance, growth, and composition during the transitional period with conventional (CONV), integrated (INT), and organic (ORG) nutrient management under four crop diversification intensities in a dry zone of Sri Lanka. Monocrop rice and a rice-maize rotation were the starting point. After 1 year, the diversification intensity was increased by adding interseason sunnhemp (rice-sunnhemp-rice and rice-sunnhemp-maize). Weed density and weed biomass were measured at 20 DAS and 60 DAS intervals. Weed density was higher in ORG during the early growth stages of monocrop rice rotation in the 1 st cycle, and monocrop rice and rice-sunnhemp-rice rotation in the 2 nd cycle while didn’t show any changes during the later growth stage of all systems in both cycles. The total weed biomass in ORG increased with increasing crop diversification. Overall, crop rotation in INT reported the lowest weed density and biomass after two cycles. In the CONV with rice-sunnhemp-maize rotation, weed biomass had declined, while in ORG grass biomass decreased only in sunnhemp cultivated rotations. Overall, INT was the best for weed suppression irrespective of crop rotation intensities. Monoculture with rice in the INT was able to suppress weed more effectively than rice-maize rotation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".